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Introduction CSCE 235 Introduction to Discrete StructuresPowerPoint Presentation

Introduction CSCE 235 Introduction to Discrete Structures

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### Introduction CSCE 235 Introduction to Discrete Structures

Spring 2010

Instructor: Berthe Y. Choueiry (Shu-we-ri)

GTA: Nobel Khandaker

http://cse.unl.edu/~choueiry/F10-235/

http://cse.unl.edu/~cse235/

Outline

- Introduction: syllabus, schedule, web, topics
- Why Discrete Mathematics?
- Basic preliminaries

Introduction

- Roll
- Syllabus
- Lectures: M/W/F 12:30—1:20 pm (Avery 119)
- Recitations: M 3:30—4:20 pm (Avery 110)
- Office hours:
- Instructor: M/W 1:30—2:30 pm (Avery 360)
- TA: Tue/Thu 9:00—10:00 am (Student Res. Center)

- Must have a cse account
- Must use cse handin
- Bonus points: report bugs
- Web page

Why Discrete Mathematics? (I)

- Computers use discrete structures to represent and manipulate data.
- CSE 235 and CSE 310 are the basic building block for becoming a Computer Scientist
- Computer Science is not Programming
- Computer Science is not Software Engineering
- Edsger Dijkstra: “Computer Science is no more about computers than Astronomy is about telescopes.”
- Computer Science is about problem solving.

Why Discrete Mathematics? (II)

- Mathematics is at the heart of problem solving
- Defining a problem requires mathematical rigor
- Use and analysis of models, data structures, algorithms requires a solid foundation of mathematics
- To justify why a particular way of solving a problem is correct or efficient (i.e., better than another way) requires analysis with a well-defined mathematical model.

Problem Solving requires mathematical rigor

- Your boss is not going to ask you to solve
- an MST (Minimal Spanning Tree) or
- a TSP (Travelling Salesperson Problem)

- Rarely will you encounter a problem in an abstract setting
- However, he/she may ask you to build a rotation of the company’s delivery trucks to minimize fuel usage
- It is up to you to determine
- a proper model for representing the problem and
- a correct or efficient algorithm for solving it

Scenario I

- A limo company has hired you/your company to write a computer program to automate the following tasks for a large event
- Task1: In the first scenario, businesses request
- limos and drivers
- for a fixed period of time, specifying a start data/time and end date/time and
- a flat charge rate

- The program must generate a schedule that accommodates the maximum number of customers

Scenario II

- Task 2: In the second scenario
- the limo service allows customers to bid on a ride
- so that the highest bidder get a limo when there aren’t enough limos available

- The program should make a schedule that
- Is feasible (no limo is assigned to two or more customers at the same time)
- While maximizing the total profit

Scenario III

- Task 3: Here each customer
- is allowed to specify a set of various times and
- bid an amount for the entire event.
- The limo service must choose to accept the entire set of times or reject it

- The program must again maximize the profit.

What’s your job?

- Build a mathematical model for each scenario
- Develop an algorithm for solving each task
- Justify that your solutions work
- Prove that your algorithms terminate. Termination
- Prove that your algorithms find a solution when there is one. Completeness
- Prove that the solution of your algorithms is correct Soundness
- Prove that your algorithms find the best solution (i.e., maximize profit). Optimality (of the solution)
- Prove that your algorithms finish before the end of life on earth. Efficiency, time & space complexity

The goal of this course

- Give you the foundations that you will use to eventually solve these problems.
- Task1 is easily (i.e., efficiently) solved by a greedy algorithm
- Task2 can also be (almost) easily solved, but requires a more involved technique, dynamic programming
- Task3 is not efficiently solvable (it is NP-hard) by any known technique. It is believed today that to guarantee an optimal solution, one needs to look at all (exponentially many) possibilities

Basic Preliminaries

- A set is a collection of objects.
- For example:
- S = {s1,s2,s3,…,sn} is a finite set of n elements
- S = {s1,s2,s3,…} is a infinite set of elements.

- s1 S denotes that the object s1 is an element of the set S
- s1 S denotes that the object s1 is not an element of the set S
- LaTex
- $S=\{s_1,s_2,s_3, \ldots,s_n\}$
- $s_i \in S$
- $si \notin S$

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